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https://github.com/ostris/ai-toolkit.git
synced 2026-04-30 03:01:28 +00:00
Made a very basic vae trainer.
This commit is contained in:
Jaret Burkett
@@ -13,9 +13,6 @@ class BaseTrainProcess(BaseProcess):
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config: OrderedDict
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):
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super().__init__(process_id, job, config)
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self.process_id = process_id
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self.job = job
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self.config = config
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def run(self):
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# implement in child class
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291
jobs/process/TrainVAEProcess.py
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291
jobs/process/TrainVAEProcess.py
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@@ -0,0 +1,291 @@
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import copy
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import os
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import time
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from collections import OrderedDict
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from PIL import Image
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from PIL.ImageOps import exif_transpose
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from safetensors.torch import save_file
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from torch.utils.data import DataLoader, ConcatDataset
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import torch
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from torch import nn
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from torchvision.transforms import transforms
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from jobs.process import BaseTrainProcess
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from toolkit.kohya_model_util import load_vae
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from toolkit.data_loader import ImageDataset
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from toolkit.metadata import get_meta_for_safetensors
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from toolkit.train_tools import get_torch_dtype
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from tqdm import tqdm
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import time
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import numpy as np
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IMAGE_TRANSFORMS = transforms.Compose(
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[
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]),
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]
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)
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INVERSE_IMAGE_TRANSFORMS = transforms.Compose(
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[
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transforms.Normalize(
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mean=[-0.5/0.5],
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std=[1/0.5]
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),
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transforms.ToPILImage(),
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]
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)
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class TrainVAEProcess(BaseTrainProcess):
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def __init__(self, process_id: int, job, config: OrderedDict):
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super().__init__(process_id, job, config)
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self.data_loader = None
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self.vae = None
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self.device = self.get_conf('device', self.job.device)
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self.vae_path = self.get_conf('vae_path', required=True)
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self.datasets_objects = self.get_conf('datasets', required=True)
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self.training_folder = self.get_conf('training_folder', self.job.training_folder)
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self.batch_size = self.get_conf('batch_size', 1)
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self.resolution = self.get_conf('resolution', 256)
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self.learning_rate = self.get_conf('learning_rate', 1e-4)
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self.sample_every = self.get_conf('sample_every', None)
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self.epochs = self.get_conf('epochs', None)
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self.max_steps = self.get_conf('max_steps', None)
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self.save_every = self.get_conf('save_every', None)
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self.dtype = self.get_conf('dtype', 'float32')
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self.sample_sources = self.get_conf('sample_sources', None)
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self.torch_dtype = get_torch_dtype(self.dtype)
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self.save_root = os.path.join(self.training_folder, self.job.name)
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if self.sample_every is not None and self.sample_sources is None:
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raise ValueError('sample_every is specified but sample_sources is not')
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if self.epochs is None and self.max_steps is None:
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raise ValueError('epochs or max_steps must be specified')
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self.data_loaders = []
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datasets = []
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# check datasets
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assert isinstance(self.datasets_objects, list)
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for dataset in self.datasets_objects:
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if 'path' not in dataset:
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raise ValueError('dataset must have a path')
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# check if is dir
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if not os.path.isdir(dataset['path']):
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raise ValueError(f"dataset path does is not a directory: {dataset['path']}")
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# make training folder
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if not os.path.exists(self.save_root):
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os.makedirs(self.save_root, exist_ok=True)
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def load_datasets(self):
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if self.data_loader is None:
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print(f"Loading datasets")
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datasets = []
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for dataset in self.datasets_objects:
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print(f" - Dataset: {dataset['path']}")
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ds = copy.copy(dataset)
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ds['resolution'] = self.resolution
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image_dataset = ImageDataset(ds)
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datasets.append(image_dataset)
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concatenated_dataset = ConcatDataset(datasets)
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self.data_loader = DataLoader(
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concatenated_dataset,
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batch_size=self.batch_size,
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shuffle=True
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)
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def get_loss(self, pred, target):
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loss_fn = nn.MSELoss()
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loss = loss_fn(pred, target)
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return loss
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def get_elbo_loss(self, pred, target, mu, log_var):
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# ELBO (Evidence Lower BOund) loss, aka variational lower bound
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reconstruction_loss = nn.MSELoss(reduction='sum')
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BCE = reconstruction_loss(pred, target) # reconstruction loss
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KLD = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp()) # KL divergence
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return BCE + KLD
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def save(self, step=None):
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if not os.path.exists(self.save_root):
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os.makedirs(self.save_root, exist_ok=True)
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step_num = ''
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if step is not None:
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# zeropad 9 digits
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step_num = f"_{str(step).zfill(9)}"
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filename = f'{self.job.name}{step_num}.safetensors'
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save_path = os.path.join(self.save_root, filename)
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# prepare meta
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save_meta = get_meta_for_safetensors(self.meta, self.job.name)
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state_dict = self.vae.state_dict()
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for key in list(state_dict.keys()):
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v = state_dict[key]
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v = v.detach().clone().to("cpu").to(torch.float32)
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state_dict[key] = v
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# having issues with meta
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save_file(state_dict, os.path.join(self.save_root, filename), save_meta)
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print(f"Saved to {os.path.join(self.save_root, filename)}")
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def sample(self, step=None):
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sample_folder = os.path.join(self.save_root, 'samples')
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if not os.path.exists(sample_folder):
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os.makedirs(sample_folder, exist_ok=True)
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with torch.no_grad():
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self.vae.encoder.eval()
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self.vae.decoder.eval()
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for i, img_url in enumerate(self.sample_sources):
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img = exif_transpose(Image.open(img_url))
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img = img.convert('RGB')
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# crop if not square
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if img.width != img.height:
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min_dim = min(img.width, img.height)
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img = img.crop((0, 0, min_dim, min_dim))
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# resize
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img = img.resize((self.resolution, self.resolution))
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input_img = img
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img = IMAGE_TRANSFORMS(img).unsqueeze(0).to(self.device, dtype=self.torch_dtype)
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decoded = self.vae(img).sample.squeeze(0)
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decoded = INVERSE_IMAGE_TRANSFORMS(decoded)
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# stack input image and decoded image
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input_img = input_img.resize((self.resolution, self.resolution))
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decoded = decoded.resize((self.resolution, self.resolution))
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output_img = Image.new('RGB', (self.resolution * 2, self.resolution))
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output_img.paste(input_img, (0, 0))
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output_img.paste(decoded, (self.resolution, 0))
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step_num = ''
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if step is not None:
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# zeropad 9 digits
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step_num = f"_{str(step).zfill(9)}"
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seconds_since_epoch = int(time.time())
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# zeropad 2 digits
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i_str = str(i).zfill(2)
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filename = f"{seconds_since_epoch}{step_num}_{i_str}.png"
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output_img.save(os.path.join(sample_folder, filename))
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self.vae.decoder.train()
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def run(self):
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super().run()
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self.load_datasets()
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max_step_epochs = self.max_steps // len(self.data_loader)
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num_epochs = self.epochs
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if num_epochs is None or num_epochs > max_step_epochs:
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num_epochs = max_step_epochs
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max_epoch_steps = len(self.data_loader) * num_epochs
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num_steps = self.max_steps
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if num_steps is None or num_steps > max_epoch_steps:
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num_steps = max_epoch_steps
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print(f"Training VAE")
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print(f" - Training folder: {self.training_folder}")
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print(f" - Batch size: {self.batch_size}")
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print(f" - Learning rate: {self.learning_rate}")
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print(f" - Epochs: {num_epochs}")
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print(f" - Max steps: {self.max_steps}")
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# load vae
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print(f"Loading VAE")
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print(f" - Loading VAE: {self.vae_path}")
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if self.vae is None:
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self.vae = load_vae(self.vae_path, dtype=self.torch_dtype)
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# set decoder to train
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self.vae.to(self.device, dtype=self.torch_dtype)
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self.vae.requires_grad_(False)
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self.vae.eval()
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self.vae.decoder.requires_grad_(True)
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self.vae.decoder.train()
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parameters = self.vae.decoder.parameters()
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optimizer = torch.optim.Adam(parameters, lr=self.learning_rate)
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# setup scheduler
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# scheduler = lr_scheduler.ConstantLR
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# todo allow other schedulers
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scheduler = torch.optim.lr_scheduler.ConstantLR(
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optimizer,
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total_iters=num_steps,
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factor=1,
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verbose=False
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)
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# setup tqdm progress bar
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progress_bar = tqdm(
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total=num_steps,
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desc='Training VAE',
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leave=True
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)
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step = 0
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# sample first
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self.sample()
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for epoch in range(num_epochs):
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if step >= num_steps:
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break
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for batch in self.data_loader:
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if step >= num_steps:
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break
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batch = batch.to(self.device, dtype=self.torch_dtype)
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# forward pass
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# with torch.no_grad():
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dgd = self.vae.encode(batch).latent_dist
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mu, logvar = dgd.mean, dgd.logvar
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latents = dgd.sample()
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latents.requires_grad_(True)
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pred = self.vae.decode(latents).sample
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loss = self.get_elbo_loss(pred, batch, mu, logvar)
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# Backward pass and optimization
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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scheduler.step()
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# update progress bar
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loss_value = loss.item()
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# get exponent like 3.54e-4
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loss_string = f"{loss_value:.2e}"
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learning_rate = optimizer.param_groups[0]['lr']
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progress_bar.set_postfix_str(f"LR: {learning_rate:.2e} Loss: {loss_string}")
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progress_bar.set_description(f"E: {epoch} - S: {step} ")
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progress_bar.update(1)
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if step != 0:
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if self.sample_every and step % self.sample_every == 0:
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# print above the progress bar
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print(f"Sampling at step {step}")
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self.sample(step)
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if self.save_every and step % self.save_every == 0:
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# print above the progress bar
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print(f"Saving at step {step}")
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self.save(step)
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step += 1
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self.save()
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pass
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@@ -3,4 +3,4 @@ from .ExtractLoconProcess import ExtractLoconProcess
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from .ExtractLoraProcess import ExtractLoraProcess
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from .BaseProcess import BaseProcess
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from .BaseTrainProcess import BaseTrainProcess
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from .TrainFineTuneProcess import TrainFineTuneProcess
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from .TrainVAEProcess import TrainVAEProcess
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